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A Smooth Transition Logit Model of The Effects of Deregulation in the Electricity Market

Author

Listed:
  • A. Stan Hurn
  • Annastiina Silvennoinen
  • Timo Teräsvirta

Abstract

The paper proposes and develops a smooth transition logit (STL) model that is designed to detect and model situations in which there is structural change in the behaviour underlying the latent index from which the binary dependent variable is constructed. The maximum likelihood estimators of the parameters of the model are derived along with their asymptotic properties and a Lagrange Multiplier test of the null hypothesis of linearity in the underlying latent index. The development of the STL model is motivated by the desire to assess the impact of deregulation in the Queensland electricity market by addressing the question of whether or not increased competition has resulted in changes in the behaviour of the spot price of electricity, specifically with respect to the well documented phenomenon of periodic abnormally high prices or price spikes. In testing this conjecture the STL model allows the timing of any change to be endogenously determined and also market participants' behavior to change gradually over time. The main results reported in the paper provide clear evidence in support of the structural change in nature and duration of price spikes in Queensland. The endogenous dating of the structural change by the STL model agrees with the institutional detail surrounding the process of deregulation and indicates that the full effect of the policy change took about a year to occur. Notwithstanding the fact that the STL model was specifically developed to tackle a problem couched in an Australian institutional framework this research will be of general interest and applicability. In particular, it is applicable to any situation in which the impact and dating of policy changes is required and where the outcome of the policy is naturally measurable as a binary variable.
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Suggested Citation

  • A. Stan Hurn & Annastiina Silvennoinen & Timo Teräsvirta, 2016. "A Smooth Transition Logit Model of The Effects of Deregulation in the Electricity Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(4), pages 707-733, June.
    • Handle: RePEc:wly:japmet:v:31:y:2016:i:4:p:707-733
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    Cited by:

    1. Wei Wei & Asger Lunde, 2023. "Identifying Risk Factors and Their Premia: A Study on Electricity Prices," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1647-1679.
    2. Murat Midilic, 2016. "Estimation Of Star-Garch Models With Iteratively Weighted Least Squares," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 16/918, Ghent University, Faculty of Economics and Business Administration.
    3. Susana Martins & Cristina Amado, 2018. "Financial Market Contagion and the Sovereign Debt Crisis: A Smooth Transition Approach," NIPE Working Papers 08/2018, NIPE - Universidade do Minho.
    4. Mwampashi, Muthe Mathias & Nikitopoulos, Christina Sklibosios & Rai, Alan, 2024. "From 30- to 5-minute settlement rule in the NEM: An early evaluation," Energy Policy, Elsevier, vol. 194(C).
    5. Lin Han & Ivor Cribben & Stefan Trueck, 2022. "Extremal Dependence in Australian Electricity Markets," Papers 2202.09970, arXiv.org.
    6. Csereklyei, Zsuzsanna & Khezr, Peyman, 2024. "How do changes in settlement periods affect wholesale market prices? Evidence from Australia's National Electricity Market," Energy Economics, Elsevier, vol. 132(C).
    7. Han, Lin & Cribben, Ivor & Trück, Stefan, 2025. "Extremal dependence in Australian electricity markets," Journal of Commodity Markets, Elsevier, vol. 39(C).
    8. González, Andrés & Teräsvirta, Timo & van Dijk, Dick & Yang, Yukai, 2005. "Panel Smooth Transition Regression Models," SSE/EFI Working Paper Series in Economics and Finance 604, Stockholm School of Economics, revised 11 Oct 2017.
    9. Campos-Martins, Susana & Amado, Cristina, 2022. "Financial market linkages and the sovereign debt crisis," Journal of International Money and Finance, Elsevier, vol. 123(C).
    10. Marie Bruguet & Arthur Thomas & Ronan Le Saout, 2025. "Weather Effects in Energy Seasonal Adjustment: An Application to France Energy Consumption," Post-Print hal-05288088, HAL.
    11. Apergis, Nicholas & Polemis, Michael, 2018. "Electricity supply shocks and economic growth across the US states: evidence from a time-varying Bayesian panel VAR model, aggregate and disaggregate energy sources," MPRA Paper 84954, University Library of Munich, Germany.
    12. Wei Wei & Asger Lunde, 2020. "Identifying Risk Factors and Their Premia: A Study on Electricity Prices," Monash Econometrics and Business Statistics Working Papers 10/20, Monash University, Department of Econometrics and Business Statistics.
    13. Mwampashi, Muthe Mathias & Nikitopoulos, Christina Sklibosios & Rai, Alan & Konstandatos, Otto, 2022. "Large-scale and rooftop solar generation in the NEM: A tale of two renewables strategies," Energy Economics, Elsevier, vol. 115(C).
    14. Urbina, Jilber, 2016. "Crecimiento del crédito en Nicaragua, ¿Crecimiento natural o boom crediticio? [Credit growth in Nicaragua: Natural growth or credit boom?]," MPRA Paper 75577, University Library of Munich, Germany, revised Nov 2016.
    15. Mardi Dungey & Ali Ghahremanlou & Ngo Van Long, 2017. "Strategic Bidding of Electric Power Generating Companies: Evidence from the Australian National Energy Market," CESifo Working Paper Series 6819, CESifo.
    16. Manner, Hans & Türk, Dennis & Eichler, Michael, 2016. "Modeling and forecasting multivariate electricity price spikes," Energy Economics, Elsevier, vol. 60(C), pages 255-265.
    17. Grossi, Luigi & Heim, Sven & Waterson, Michael, 2017. "The impact of the German response to the Fukushima earthquake," Energy Economics, Elsevier, vol. 66(C), pages 450-465.

    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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